We propose an approach to estimate the 6DOF pose of a satellite, relative to
a canonical pose, from a single image. Such a problem is crucial in many space
proximity operations, such as docking, debris removal, and inter-spacecraft
communications. Our approach combines machine learning and geometric
optimisation, by predicting the coordinates of a set of landmarks in the input
image, associating the landmarks to their corresponding 3D points on an a
priori reconstructed 3D model, then solving for the object pose using
non-linear optimisation. Our approach is not only novel for this specific pose
estimation task, which helps to further open up a relatively new domain for
machine learning and computer vision, but it also demonstrates superior
accuracy and won the first place in the recent Kelvins Pose Estimation
Challenge organised by the European Space Agency (ESA).

我们提出了一种从单个图像中估计卫星 6DOF 姿态的方法，该方法结合了机器学习和几何优化，并预测了输入图像中一组地标的坐标，解决了非线性优化以求解物体姿态，不仅在该特定姿态估计任务中具有新颖性，还在欧洲航天局（ESA）组织的 Kelvins 姿态估计挑战赛中展现出优秀的精确度，获得了第一名。